Phenotypic Analyses and GWAS for a Cassava Population
CIAT Training
Guilherme da Silva Pereira and João Ricardo Rosa
November 16, 2023
Installing and loading R Packages:
package.cran <- c("tidyverse", "sommer", "PerformanceAnalytics", "vcfR", "ggplot2", "bigmemory", "RColorBrewer", "factoextra", "rMVP", "statgenGWAS")
missing.cran <- setdiff(package.cran, rownames(installed.packages()))
if(length(missing.cran) > 0) install.packages(c(missing.cran), dep = TRUE)
invisible(lapply(c(package.cran), library, character.only = TRUE))Introduction
This tutorial reports the phenotypic data analysis and genetic mapping for a cassava population, which is part of the mapping current studies developed by the CIAT Breeding Program, Colombia. Our aim is to use this population to provide an introductory training on quantitative genetics to the CIAT Breeding Team.
The used population consists of 381 individuals belonging to six full-sib families, which have been phenotyped for cooking quality traits. A total of seven parents were crossed to generate these six families:
- SM3759-36 x VEN25
- COL2246 x COL1722
- COL1910 x SM3759-36
- COL1910 x COL1505
- COL1505 x COL1910
- VEN208 x VEN25
Four parents and 226 progenies were genotyped for 8,590,486 single nucleotide polymorphism (SNP) markers (BGI), distributed in the entire genome (18 chromosomes + scaffolds) stored in variant call format (VCF) file. The VCF also has SNPs called together with a second population (370 clones) focused on beta-carotene content. For the sake of time, we’ll onlly deal with the first population.
The present training is structured as follows:
- phenotypic analysis of quality traits via mixed models, using three specific traits (DM_NIRS, WAB_20min_mean, and WAB_30min_mean) evaluated in four trials during years 2020 and 2021
- construction of genetic linkage map for one of the five full-sib families (SM3759-36 x VEN25), which presented the larger amount of genotyped progenies (\(n=49\)) and both parents genotyped among all families
- quantitative trait loci (QTL) mapping, via composite interval mapping (CIM), for the three cooking quality traits
- genome-wide association study (GWAS) for the same traits, considering all the five full-sib families.
Also for the sake of time, we’ll use a subset of 9,000 SNP ($$0.1%) markers randomly sampled out of the 8.5M markers.
Phenotypic Analyses via Mixed Models
Data and Exploratory Analysis
data.pheno <- read.csv("01_RTBfoods_progeny_2020_2021_4_env 2022-05-08.csv")
head(data.pheno)## plot_name trial_harvest plot_number rep_number
## 1 202002CQQU1_ciat_rep1_GM13159-1_1 D1_1st 1 1
## 2 202002CQQU1_ciat_rep1_GM13159-4_2 D1_1st 2 1
## 3 202002CQQU1_ciat_rep1_GM13159-6_3 D1_1st 3 1
## 4 202002CQQU1_ciat_rep1_GM13159-7_4 D1_1st 4 1
## 5 202002CQQU1_ciat_rep1_GM13159-8_5 D1_1st 5 1
## 6 202002CQQU1_ciat_rep1_GM13159-9_6 D1_1st 6 1
## accession_name col_number row_number check number_germinated number_planted
## 1 GM13159-1 1 1 NA 6 6
## 2 GM13159-4 1 2 NA 5 4
## 3 GM13159-6 1 3 NA 6 6
## 4 GM13159-7 1 4 NA 4 4
## 5 GM13159-8 1 5 NA 6 6
## 6 GM13159-9 1 6 NA 5 5
## germ_perc lodging vigor number_branch plant_type root_color root_type
## 1 100 1 NA 3 3 3 3
## 2 125 1 NA 4 3 3 3
## 3 100 1 NA 2 4 3 2
## 4 100 1 NA 4 3 3 2
## 5 100 1 NA 3 3 3 2
## 6 100 1 NA 2 4 3 3
## DM_NIRS DM_oven_1 DM_oven_2 DM_oven_mean HCN_rep1 HCN_rep2 HCN_mean
## 1 40.97823 41.34744 41.46142 41.40443 NA NA NA
## 2 41.77826 42.61983 42.56000 42.58992 NA NA NA
## 3 45.29284 43.47788 43.66805 43.57296 NA NA NA
## 4 43.35637 42.63282 42.66809 42.65046 NA NA NA
## 5 43.76374 42.55326 43.01971 42.78648 NA NA NA
## 6 43.24713 42.48462 42.81179 42.64821 NA NA NA
## WAB_20min_rep1 WAB_20min_rep2 WAB_20min_mean WAB_30min_rep1 WAB_30min_rep2
## 1 1.454041 1.746310 1.600175 3.451960 5.019413
## 2 1.811940 2.430337 2.121139 3.280565 3.582652
## 3 4.184988 4.113949 4.149469 12.017386 13.360680
## 4 2.244308 2.270785 2.257546 5.345699 5.129318
## 5 2.515546 2.367617 2.441581 5.016959 4.175153
## 6 2.202935 2.200254 2.201595 5.122711 4.359793
## WAB_30min_mean
## 1 4.235686
## 2 3.431608
## 3 12.689033
## 4 5.237508
## 5 4.596056
## 6 4.741252str(data.pheno)## 'data.frame': 1468 obs. of 30 variables:
## $ plot_name : chr "202002CQQU1_ciat_rep1_GM13159-1_1" "202002CQQU1_ciat_rep1_GM13159-4_2" "202002CQQU1_ciat_rep1_GM13159-6_3" "202002CQQU1_ciat_rep1_GM13159-7_4" ...
## $ trial_harvest : chr "D1_1st" "D1_1st" "D1_1st" "D1_1st" ...
## $ plot_number : int 1 2 3 4 5 6 7 8 9 10 ...
## $ rep_number : int 1 1 1 1 1 1 1 1 1 1 ...
## $ accession_name : chr "GM13159-1" "GM13159-4" "GM13159-6" "GM13159-7" ...
## $ col_number : int 1 1 1 1 1 1 1 1 2 2 ...
## $ row_number : int 1 2 3 4 5 6 7 8 8 7 ...
## $ check : int NA NA NA NA NA NA NA NA NA 1 ...
## $ number_germinated: int 6 5 6 4 6 5 5 6 6 3 ...
## $ number_planted : int 6 4 6 4 6 5 6 6 6 6 ...
## $ germ_perc : num 100 125 100 100 100 ...
## $ lodging : int 1 1 1 1 1 1 1 1 1 1 ...
## $ vigor : int NA NA NA NA NA NA NA NA NA NA ...
## $ number_branch : int 3 4 2 4 3 2 3 4 2 2 ...
## $ plant_type : int 3 3 4 3 3 4 4 4 3 5 ...
## $ root_color : int 3 3 3 3 3 3 2 3 3 3 ...
## $ root_type : int 3 3 2 2 2 3 2 3 3 5 ...
## $ DM_NIRS : num 41 41.8 45.3 43.4 43.8 ...
## $ DM_oven_1 : num 41.3 42.6 43.5 42.6 42.6 ...
## $ DM_oven_2 : num 41.5 42.6 43.7 42.7 43 ...
## $ DM_oven_mean : num 41.4 42.6 43.6 42.7 42.8 ...
## $ HCN_rep1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ HCN_rep2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ HCN_mean : num NA NA NA NA NA NA NA NA NA NA ...
## $ WAB_20min_rep1 : num 1.45 1.81 4.18 2.24 2.52 ...
## $ WAB_20min_rep2 : num 1.75 2.43 4.11 2.27 2.37 ...
## $ WAB_20min_mean : num 1.6 2.12 4.15 2.26 2.44 ...
## $ WAB_30min_rep1 : num 3.45 3.28 12.02 5.35 5.02 ...
## $ WAB_30min_rep2 : num 5.02 3.58 13.36 5.13 4.18 ...
## $ WAB_30min_mean : num 4.24 3.43 12.69 5.24 4.6 ...data.pheno <- data.pheno %>% arrange(trial_harvest, rep_number, col_number, row_number, accession_name)
factors <- c("trial_harvest", "rep_number", "accession_name")
data.pheno[,factors] <- lapply(data.pheno[,factors], factor)
str(data.pheno)## 'data.frame': 1468 obs. of 30 variables:
## $ plot_name : chr "202002CQQU1_ciat_rep1_GM13159-1_1" "202002CQQU1_ciat_rep1_GM13159-4_2" "202002CQQU1_ciat_rep1_GM13159-6_3" "202002CQQU1_ciat_rep1_GM13159-7_4" ...
## $ trial_harvest : Factor w/ 4 levels "D1_1st","D1_2nd",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ plot_number : int 1 2 3 4 5 6 7 8 16 15 ...
## $ rep_number : Factor w/ 9 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ accession_name : Factor w/ 390 levels "COL1505","COL1722",..: 6 36 55 56 57 58 8 9 16 15 ...
## $ col_number : int 1 1 1 1 1 1 1 1 2 2 ...
## $ row_number : int 1 2 3 4 5 6 7 8 1 2 ...
## $ check : int NA NA NA NA NA NA NA NA NA NA ...
## $ number_germinated: int 6 5 6 4 6 5 5 6 6 5 ...
## $ number_planted : int 6 4 6 4 6 5 6 6 6 6 ...
## $ germ_perc : num 100 125 100 100 100 ...
## $ lodging : int 1 1 1 1 1 1 1 1 1 1 ...
## $ vigor : int NA NA NA NA NA NA NA NA NA NA ...
## $ number_branch : int 3 4 2 4 3 2 3 4 4 3 ...
## $ plant_type : int 3 3 4 3 3 4 4 4 3 3 ...
## $ root_color : int 3 3 3 3 3 3 2 3 3 3 ...
## $ root_type : int 3 3 2 2 2 3 2 3 3 3 ...
## $ DM_NIRS : num 41 41.8 45.3 43.4 43.8 ...
## $ DM_oven_1 : num 41.3 42.6 43.5 42.6 42.6 ...
## $ DM_oven_2 : num 41.5 42.6 43.7 42.7 43 ...
## $ DM_oven_mean : num 41.4 42.6 43.6 42.7 42.8 ...
## $ HCN_rep1 : num NA NA NA NA NA NA NA NA NA NA ...
## $ HCN_rep2 : num NA NA NA NA NA NA NA NA NA NA ...
## $ HCN_mean : num NA NA NA NA NA NA NA NA NA NA ...
## $ WAB_20min_rep1 : num 1.45 1.81 4.18 2.24 2.52 ...
## $ WAB_20min_rep2 : num 1.75 2.43 4.11 2.27 2.37 ...
## $ WAB_20min_mean : num 1.6 2.12 4.15 2.26 2.44 ...
## $ WAB_30min_rep1 : num 3.45 3.28 12.02 5.35 5.02 ...
## $ WAB_30min_rep2 : num 5.02 3.58 13.36 5.13 4.18 ...
## $ WAB_30min_mean : num 4.24 3.43 12.69 5.24 4.6 ...table(data.pheno$trial_harvest)##
## D1_1st D1_2nd P1_1st P1_2nd
## 334 334 400 400colnames(data.pheno)## [1] "plot_name" "trial_harvest" "plot_number"
## [4] "rep_number" "accession_name" "col_number"
## [7] "row_number" "check" "number_germinated"
## [10] "number_planted" "germ_perc" "lodging"
## [13] "vigor" "number_branch" "plant_type"
## [16] "root_color" "root_type" "DM_NIRS"
## [19] "DM_oven_1" "DM_oven_2" "DM_oven_mean"
## [22] "HCN_rep1" "HCN_rep2" "HCN_mean"
## [25] "WAB_20min_rep1" "WAB_20min_rep2" "WAB_20min_mean"
## [28] "WAB_30min_rep1" "WAB_30min_rep2" "WAB_30min_mean"traits <- colnames(data.pheno)[9:30]
traits## [1] "number_germinated" "number_planted" "germ_perc"
## [4] "lodging" "vigor" "number_branch"
## [7] "plant_type" "root_color" "root_type"
## [10] "DM_NIRS" "DM_oven_1" "DM_oven_2"
## [13] "DM_oven_mean" "HCN_rep1" "HCN_rep2"
## [16] "HCN_mean" "WAB_20min_rep1" "WAB_20min_rep2"
## [19] "WAB_20min_mean" "WAB_30min_rep1" "WAB_30min_rep2"
## [22] "WAB_30min_mean"Let’s take a look of checks used in the trials:
sort(table(data.pheno$accession_name))##
## GM13159-10 GM13159-15 GM13159-16 GM13159-2 GM13159-29 GM13159-3
## 2 2 2 2 2 2
## GM13159-31 GM13159-34 GM13159-35 GM13159-37 GM13159-41 GM13159-42
## 2 2 2 2 2 2
## GM13159-44 GM13159-45 GM13159-48 GM13159-5 GM13159-52 GM13169-1
## 2 2 2 2 2 2
## GM13169-15 GM13169-20 GM13169-29 GM13169-4 GM13169-5 GM13174-25
## 2 2 2 2 2 2
## GM13174-26 GM13174-52 GM13174-8 GM13175-10 GM13175-12 GM13175-13
## 2 2 2 2 2 2
## GM13175-14 GM13175-15 GM13175-17 GM13175-2 GM13175-20 GM13175-21
## 2 2 2 2 2 2
## GM13175-23 GM13175-24 GM13175-31 GM13175-32 GM13175-33 GM13175-35
## 2 2 2 2 2 2
## GM13175-36 GM13175-39 GM13175-47 GM13175-49 GM13196-1 GM13196-100
## 2 2 2 2 2 2
## GM13196-101 GM13196-102 GM13196-104 GM13196-106 GM13196-109 GM13196-110
## 2 2 2 2 2 2
## GM13196-112 GM13196-113 GM13196-115 GM13196-116 GM13196-121 GM13196-123
## 2 2 2 2 2 2
## GM13196-125 GM13196-14 GM13196-140 GM13196-143 GM13196-147 GM13196-148
## 2 2 2 2 2 2
## GM13196-149 GM13196-150 GM13196-151 GM13196-155 GM13196-156 GM13196-160
## 2 2 2 2 2 2
## GM13196-163 GM13196-21 GM13196-23 GM13196-24 GM13196-26 GM13196-32
## 2 2 2 2 2 2
## GM13196-42 GM13196-43 GM13196-44 GM13196-45 GM13196-46 GM13196-47
## 2 2 2 2 2 2
## GM13196-48 GM13196-49 GM13196-5 GM13196-53 GM13196-54 GM13196-56
## 2 2 2 2 2 2
## GM13196-57 GM13196-58 GM13196-59 GM13196-61 GM13196-64 GM13196-65
## 2 2 2 2 2 2
## GM13196-66 GM13196-70 GM13196-72 GM13196-76 GM13196-77 GM13196-80
## 2 2 2 2 2 2
## GM13196-81 GM13196-83 GM13196-85 GM13196-87 GM13196-88 GM13196-89
## 2 2 2 2 2 2
## GM13196-90 GM13196-91 GM13196-92 GM13196-93 GM13196-94 GM13196-95
## 2 2 2 2 2 2
## GM13196-96 GM13196-97 GM13196-98 GM13240-10 GM13240-11 GM13240-12
## 2 2 2 2 2 2
## GM13240-16 GM13240-20 GM13240-22 GM13240-31 GM13240-35 GM13240-36
## 2 2 2 2 2 2
## GM13240-37 GM13240-45 GM13240-5 GM13240-57 GM13240-59 GM13240-61
## 2 2 2 2 2 2
## GM13240-7 GM13240-9 GM13159-1 GM13159-11 GM13159-12 GM13159-13
## 2 2 4 4 4 4
## GM13159-14 GM13159-17 GM13159-18 GM13159-19 GM13159-20 GM13159-21
## 4 4 4 4 4 4
## GM13159-22 GM13159-23 GM13159-24 GM13159-25 GM13159-26 GM13159-27
## 4 4 4 4 4 4
## GM13159-28 GM13159-30 GM13159-32 GM13159-33 GM13159-4 GM13159-43
## 4 4 4 4 4 4
## GM13159-46 GM13159-47 GM13159-49 GM13159-50 GM13159-51 GM13159-53
## 4 4 4 4 4 4
## GM13159-54 GM13159-55 GM13159-56 GM13159-57 GM13159-6 GM13159-7
## 4 4 4 4 4 4
## GM13159-8 GM13159-9 GM13169-10 GM13169-11 GM13169-12 GM13169-13
## 4 4 4 4 4 4
## GM13169-14 GM13169-16 GM13169-18 GM13169-19 GM13169-2 GM13169-21
## 4 4 4 4 4 4
## GM13169-22 GM13169-23 GM13169-24 GM13169-25 GM13169-26 GM13169-7
## 4 4 4 4 4 4
## GM13169-8 GM13169-9 GM13174-1 GM13174-10 GM13174-11 GM13174-12
## 4 4 4 4 4 4
## GM13174-13 GM13174-14 GM13174-15 GM13174-16 GM13174-17 GM13174-18
## 4 4 4 4 4 4
## GM13174-2 GM13174-20 GM13174-21 GM13174-22 GM13174-23 GM13174-24
## 4 4 4 4 4 4
## GM13174-27 GM13174-28 GM13174-29 GM13174-3 GM13174-30 GM13174-31
## 4 4 4 4 4 4
## GM13174-32 GM13174-33 GM13174-34 GM13174-35 GM13174-36 GM13174-37
## 4 4 4 4 4 4
## GM13174-38 GM13174-39 GM13174-4 GM13174-40 GM13174-41 GM13174-42
## 4 4 4 4 4 4
## GM13174-43 GM13174-44 GM13174-45 GM13174-46 GM13174-47 GM13174-48
## 4 4 4 4 4 4
## GM13174-49 GM13174-50 GM13174-51 GM13174-53 GM13174-54 GM13174-55
## 4 4 4 4 4 4
## GM13174-56 GM13174-6 GM13174-7 GM13174-9 GM13175-1 GM13175-16
## 4 4 4 4 4 4
## GM13175-19 GM13175-22 GM13175-25 GM13175-26 GM13175-27 GM13175-28
## 4 4 4 4 4 4
## GM13175-29 GM13175-3 GM13175-30 GM13175-38 GM13175-4 GM13175-40
## 4 4 4 4 4 4
## GM13175-42 GM13175-43 GM13175-44 GM13175-45 GM13175-46 GM13175-48
## 4 4 4 4 4 4
## GM13175-5 GM13175-6 GM13175-7 GM13175-8 GM13175-9 GM13196-10
## 4 4 4 4 4 4
## GM13196-105 GM13196-107 GM13196-111 GM13196-118 GM13196-119 GM13196-12
## 4 4 4 4 4 4
## GM13196-120 GM13196-122 GM13196-124 GM13196-126 GM13196-127 GM13196-128
## 4 4 4 4 4 4
## GM13196-129 GM13196-13 GM13196-130 GM13196-131 GM13196-132 GM13196-133
## 4 4 4 4 4 4
## GM13196-134 GM13196-135 GM13196-136 GM13196-137 GM13196-138 GM13196-139
## 4 4 4 4 4 4
## GM13196-141 GM13196-142 GM13196-145 GM13196-152 GM13196-153 GM13196-154
## 4 4 4 4 4 4
## GM13196-157 GM13196-158 GM13196-159 GM13196-16 GM13196-161 GM13196-162
## 4 4 4 4 4 4
## GM13196-164 GM13196-165 GM13196-166 GM13196-2 GM13196-20 GM13196-27
## 4 4 4 4 4 4
## GM13196-28 GM13196-29 GM13196-3 GM13196-30 GM13196-31 GM13196-33
## 4 4 4 4 4 4
## GM13196-35 GM13196-36 GM13196-37 GM13196-38 GM13196-39 GM13196-4
## 4 4 4 4 4 4
## GM13196-41 GM13196-52 GM13196-60 GM13196-62 GM13196-67 GM13196-68
## 4 4 4 4 4 4
## GM13196-7 GM13196-73 GM13196-74 GM13196-75 GM13196-78 GM13196-79
## 4 4 4 4 4 4
## GM13196-8 GM13196-82 GM13196-84 GM13196-9 GM13196-99 GM13240-1
## 4 4 4 4 4 4
## GM13240-13 GM13240-14 GM13240-15 GM13240-17 GM13240-18 GM13240-19
## 4 4 4 4 4 4
## GM13240-2 GM13240-21 GM13240-23 GM13240-24 GM13240-25 GM13240-26
## 4 4 4 4 4 4
## GM13240-27 GM13240-28 GM13240-29 GM13240-3 GM13240-30 GM13240-32
## 4 4 4 4 4 4
## GM13240-33 GM13240-34 GM13240-38 GM13240-39 GM13240-40 GM13240-41
## 4 4 4 4 4 4
## GM13240-42 GM13240-43 GM13240-44 GM13240-46 GM13240-47 GM13240-48
## 4 4 4 4 4 4
## GM13240-49 GM13240-51 GM13240-52 GM13240-53 GM13240-54 GM13240-55
## 4 4 4 4 4 4
## GM13240-56 GM13240-58 GM13240-6 GM13240-60 GM13240-62 GM13240-63
## 4 4 4 4 4 4
## GM13240-64 GM13240-65 GM13240-8 COL1505 COL2246 CR138
## 4 4 4 8 8 8
## SM3759-36 VEN208 PER183 COL1722 VEN25 COL1910
## 8 8 38 44 44 46The checks consist of seven full-sib parents and the individuals CR138 and PER183.
table(data.pheno$check)##
## 1
## 68data.pheno$check[which(substr(data.pheno$accession_name, 1, 2) == "GM")] <- 0
data.pheno$check[which(substr(data.pheno$accession_name, 1, 2) != "GM")] <- 1
table(data.pheno$check)##
## 0 1
## 1256 212data.pheno$pop <- abs(data.pheno$check - 1)
table(data.pheno$pop)##
## 0 1
## 212 1256Boxplots for all traits:
long.data <- reshape2::melt(data.pheno, measure.vars = traits)
head(long.data)## plot_name trial_harvest plot_number rep_number
## 1 202002CQQU1_ciat_rep1_GM13159-1_1 D1_1st 1 1
## 2 202002CQQU1_ciat_rep1_GM13159-4_2 D1_1st 2 1
## 3 202002CQQU1_ciat_rep1_GM13159-6_3 D1_1st 3 1
## 4 202002CQQU1_ciat_rep1_GM13159-7_4 D1_1st 4 1
## 5 202002CQQU1_ciat_rep1_GM13159-8_5 D1_1st 5 1
## 6 202002CQQU1_ciat_rep1_GM13159-9_6 D1_1st 6 1
## accession_name col_number row_number check pop variable value
## 1 GM13159-1 1 1 0 1 number_germinated 6
## 2 GM13159-4 1 2 0 1 number_germinated 5
## 3 GM13159-6 1 3 0 1 number_germinated 6
## 4 GM13159-7 1 4 0 1 number_germinated 4
## 5 GM13159-8 1 5 0 1 number_germinated 6
## 6 GM13159-9 1 6 0 1 number_germinated 5ggplot(data = long.data, aes(x = trial_harvest, y = value)) +
geom_boxplot() +
geom_point(data = long.data %>% filter(check == 1), aes(color = accession_name), position = "jitter", alpha = 0.5) +
facet_wrap(~ variable, scales = "free_y", nrow = 3) + theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.position = "bottom")## Warning: Removed 12204 rows containing non-finite values (stat_boxplot).## Warning: Removed 1798 rows containing missing values (geom_point).
Boxplots for cooking quality-related traits:
traits <- traits[-c(1:9)]
long.data <- long.data %>% filter(variable %in% traits)
long.data$variable <- factor(long.data$variable, levels = traits)
head(long.data)## plot_name trial_harvest plot_number rep_number
## 1 202002CQQU1_ciat_rep1_GM13159-1_1 D1_1st 1 1
## 2 202002CQQU1_ciat_rep1_GM13159-4_2 D1_1st 2 1
## 3 202002CQQU1_ciat_rep1_GM13159-6_3 D1_1st 3 1
## 4 202002CQQU1_ciat_rep1_GM13159-7_4 D1_1st 4 1
## 5 202002CQQU1_ciat_rep1_GM13159-8_5 D1_1st 5 1
## 6 202002CQQU1_ciat_rep1_GM13159-9_6 D1_1st 6 1
## accession_name col_number row_number check pop variable value
## 1 GM13159-1 1 1 0 1 DM_NIRS 40.97823
## 2 GM13159-4 1 2 0 1 DM_NIRS 41.77826
## 3 GM13159-6 1 3 0 1 DM_NIRS 45.29284
## 4 GM13159-7 1 4 0 1 DM_NIRS 43.35637
## 5 GM13159-8 1 5 0 1 DM_NIRS 43.76374
## 6 GM13159-9 1 6 0 1 DM_NIRS 43.24713ggplot(data = long.data, aes(x = trial_harvest, y = value)) +
geom_boxplot() +
geom_point(data = long.data %>% filter(check == 1), aes(color = accession_name), position = "jitter", alpha = 0.5) +
facet_wrap(~ variable, scales = "free_y", nrow = 2) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1), legend.position = "bottom")## Warning: Removed 11042 rows containing non-finite values (stat_boxplot).## Warning: Removed 1654 rows containing missing values (geom_point).
Mixed Model Analysis
For the selected traits (DM_NIRS, WAB_20min_mean, and WAB_30min_mean), the mixed model below will be fitted to estimate (i) variance components, (ii) broad-sense heritability (plant level), and (iii) predictions by treating genotype as random effect. Genetic correlations will be also estimated using the predictions. Additionally, the genotype effects will be declared as a fixed term in the model, and the individual’s best linear unbiased estimates (BLUEs) will be used for QTL mapping purposes. The aim is to avoid double shrinkage due to the inclusion of random genetic effects specifically in the GWAS model.
\[y_{ij} = \mu + t_j + g_i + f(r,c) + \varepsilon_{ij}\] where - \(y_{ij}\) is the phenotypic observation of genotype \(i\) in trial \(j\), - \(t_j\) is the fixed effect of trial \(j\) (\(j=1,\dots,J\); \(J=4\)), - \(g_i\) is the effect of genotype \(i\) separated into random effects of progenies (\(i=1,\dots,I_g\); \(I_g=381\), with \(g_i \sim N(0, \sigma^2_g)\), and fixed effects of parents and checks (\(i=I_g+1,\dots,I_g+I_c\); \(I_c=9\), - \(f(r,c)\) is the random effect of spatial trends considering the row and column levels, and - \(\varepsilon_{ij}\) is the residual random error with \(\varepsilon_{ij} \sim N(0, \sigma^2)\).
DM_NIRS
model.DM <- mmer(DM_NIRS ~ trial_harvest + check:accession_name,
random = ~ pop:accession_name + spl2D(row_number, col_number, at.levels = trial_harvest),
rcov = ~ vs(ds(trial_harvest), units),
data = data.pheno, verbose = TRUE)## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
## iteration LogLik wall cpu(sec) restrained
## 1 -142.569 2:35:5 9 0
## 2 -43.9087 2:35:14 18 0
## 3 -31.4041 2:35:23 27 0
## 4 -29.78 2:35:32 36 0
## 5 -29.6896 2:35:41 45 0
## 6 -29.6827 2:35:50 54 0
## 7 -29.682 2:35:58 62 0plot(model.DM)## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
summary(model.DM)## ==========================================================================================================
## Multivariate Linear Mixed Model fit by REML
## ********************************************* sommer 4.1 *********************************************
## ==========================================================================================================
## logLik AIC BIC Method Converge
## Value -29.68204 85.36409 150.2494 NR TRUE
## ==========================================================================================================
## Variance-Covariance components:
## VarComp
## pop:accession_name.DM_NIRS-DM_NIRS 14.188
## spl2D(row_number, col_number, at.levels = trial_harvest).DM_NIRS-DM_NIRS 21.849
## D1_1st:units.DM_NIRS-DM_NIRS 14.472
## D1_2nd:units.DM_NIRS-DM_NIRS 21.544
## P1_1st:units.DM_NIRS-DM_NIRS 7.782
## P1_2nd:units.DM_NIRS-DM_NIRS 8.788
## VarCompSE
## pop:accession_name.DM_NIRS-DM_NIRS 1.5273
## spl2D(row_number, col_number, at.levels = trial_harvest).DM_NIRS-DM_NIRS 5.0606
## D1_1st:units.DM_NIRS-DM_NIRS 1.6587
## D1_2nd:units.DM_NIRS-DM_NIRS 2.3338
## P1_1st:units.DM_NIRS-DM_NIRS 0.9799
## P1_2nd:units.DM_NIRS-DM_NIRS 1.1740
## Zratio
## pop:accession_name.DM_NIRS-DM_NIRS 9.290
## spl2D(row_number, col_number, at.levels = trial_harvest).DM_NIRS-DM_NIRS 4.317
## D1_1st:units.DM_NIRS-DM_NIRS 8.725
## D1_2nd:units.DM_NIRS-DM_NIRS 9.231
## P1_1st:units.DM_NIRS-DM_NIRS 7.942
## P1_2nd:units.DM_NIRS-DM_NIRS 7.485
## Constraint
## pop:accession_name.DM_NIRS-DM_NIRS Positive
## spl2D(row_number, col_number, at.levels = trial_harvest).DM_NIRS-DM_NIRS Positive
## D1_1st:units.DM_NIRS-DM_NIRS Positive
## D1_2nd:units.DM_NIRS-DM_NIRS Positive
## P1_1st:units.DM_NIRS-DM_NIRS Positive
## P1_2nd:units.DM_NIRS-DM_NIRS Positive
## ==========================================================================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 DM_NIRS (Intercept) 40.1477 0.3907 102.7568
## 2 DM_NIRS trial_harvestD1_2nd -2.0683 0.3928 -5.2657
## 3 DM_NIRS trial_harvestP1_1st 5.6941 0.3122 18.2406
## 4 DM_NIRS trial_harvestP1_2nd 2.2380 0.3368 6.6448
## 5 DM_NIRS check:accession_nameCOL1505 0.3559 1.3804 0.2578
## 6 DM_NIRS check:accession_nameCOL1722 5.8834 0.6592 8.9246
## 7 DM_NIRS check:accession_nameCOL1910 -11.3395 0.6787 -16.7075
## 8 DM_NIRS check:accession_nameCOL2246 -6.0558 1.3315 -4.5480
## ... please access the object to see more
## ==========================================================================================================
## Groups and observations:
## DM_NIRS
## pop:accession_name 390
## spl2D(row_number, col_number, at.levels = trial_harvest) 168
## ==========================================================================================================
## Use the '$' sign to access results and parameterscoef.mmer(model.DM)## Trait Effect Estimate
## 1 DM_NIRS (Intercept) 40.1476897
## 2 DM_NIRS trial_harvestD1_2nd -2.0682826
## 3 DM_NIRS trial_harvestP1_1st 5.6941482
## 4 DM_NIRS trial_harvestP1_2nd 2.2380421
## 5 DM_NIRS check:accession_nameCOL1505 0.3559151
## 6 DM_NIRS check:accession_nameCOL1722 5.8834301
## 7 DM_NIRS check:accession_nameCOL1910 -11.3395260
## 8 DM_NIRS check:accession_nameCOL2246 -6.0557938
## 9 DM_NIRS check:accession_nameCR138 -4.7099465
## 10 DM_NIRS check:accession_namePER183 -3.9511592
## 11 DM_NIRS check:accession_nameSM3759-36 -2.4881537
## 12 DM_NIRS check:accession_nameVEN208 1.9845716
## 13 DM_NIRS check:accession_nameVEN25 4.4996748summary(model.DM)$varcomp## VarComp
## pop:accession_name.DM_NIRS-DM_NIRS 14.187650
## spl2D(row_number, col_number, at.levels = trial_harvest).DM_NIRS-DM_NIRS 21.848932
## D1_1st:units.DM_NIRS-DM_NIRS 14.472425
## D1_2nd:units.DM_NIRS-DM_NIRS 21.543719
## P1_1st:units.DM_NIRS-DM_NIRS 7.782145
## P1_2nd:units.DM_NIRS-DM_NIRS 8.787567
## VarCompSE
## pop:accession_name.DM_NIRS-DM_NIRS 1.5272780
## spl2D(row_number, col_number, at.levels = trial_harvest).DM_NIRS-DM_NIRS 5.0605857
## D1_1st:units.DM_NIRS-DM_NIRS 1.6586775
## D1_2nd:units.DM_NIRS-DM_NIRS 2.3338287
## P1_1st:units.DM_NIRS-DM_NIRS 0.9799001
## P1_2nd:units.DM_NIRS-DM_NIRS 1.1740350
## Zratio
## pop:accession_name.DM_NIRS-DM_NIRS 9.289501
## spl2D(row_number, col_number, at.levels = trial_harvest).DM_NIRS-DM_NIRS 4.317471
## D1_1st:units.DM_NIRS-DM_NIRS 8.725280
## D1_2nd:units.DM_NIRS-DM_NIRS 9.231063
## P1_1st:units.DM_NIRS-DM_NIRS 7.941774
## P1_2nd:units.DM_NIRS-DM_NIRS 7.484927
## Constraint
## pop:accession_name.DM_NIRS-DM_NIRS Positive
## spl2D(row_number, col_number, at.levels = trial_harvest).DM_NIRS-DM_NIRS Positive
## D1_1st:units.DM_NIRS-DM_NIRS Positive
## D1_2nd:units.DM_NIRS-DM_NIRS Positive
## P1_1st:units.DM_NIRS-DM_NIRS Positive
## P1_2nd:units.DM_NIRS-DM_NIRS Positiveheritability.DM <- vpredict(model.DM, h2 ~ (V1) / (V1 + (V3+V4+V5+V6)/4))
heritability.DM## Estimate SE
## h2 0.5190455 0.03207124checks <- paste("pop:accession_name", unique(data.pheno$accession_name[data.pheno$check == 1]), sep = "")
BLUPs.DM <- coef.mmer(model.DM)$Estimate[1] + randef(model.DM)[[1]]$DM_NIRS[!names(randef(model.DM)[[1]]$DM_NIRS) %in% checks]
head(BLUPs.DM, 10)## pop:accession_nameGM13159-1 pop:accession_nameGM13159-10
## 35.87008 32.68062
## pop:accession_nameGM13159-11 pop:accession_nameGM13159-12
## 37.75610 34.68457
## pop:accession_nameGM13159-13 pop:accession_nameGM13159-14
## 35.84614 42.33356
## pop:accession_nameGM13159-15 pop:accession_nameGM13159-16
## 44.24813 44.18083
## pop:accession_nameGM13159-17 pop:accession_nameGM13159-18
## 42.98113 36.27245length(BLUPs.DM)## [1] 381WAB_20min_mean
model.WAB20 <- mmer(WAB_20min_mean ~ trial_harvest + check:accession_name,
random = ~ pop:accession_name + spl2D(row_number, col_number, at.levels = trial_harvest),
rcov = ~vs(ds(trial_harvest), units),
data = data.pheno, verbose = TRUE)## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
## iteration LogLik wall cpu(sec) restrained
## 1 -296.354 2:36:19 5 0
## 2 -246.988 2:36:23 9 0
## 3 -235.283 2:36:27 13 0
## 4 -233.316 2:36:31 17 0
## 5 -233.023 2:36:35 21 0
## 6 -232.978 2:36:40 26 0
## 7 -232.971 2:36:44 30 0
## 8 -232.97 2:36:48 34 0
## 9 -232.969 2:36:52 38 0plot(model.WAB20)## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
summary(model.WAB20)## ========================================================================================================================
## Multivariate Linear Mixed Model fit by REML
## **************************************************** sommer 4.1 ****************************************************
## ========================================================================================================================
## logLik AIC BIC Method Converge
## Value -232.9695 491.939 554.0039 NR TRUE
## ========================================================================================================================
## Variance-Covariance components:
## VarComp
## pop:accession_name.WAB_20min_mean-WAB_20min_mean 1.1052
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_20min_mean-WAB_20min_mean 1.0793
## D1_1st:units.WAB_20min_mean-WAB_20min_mean 2.3875
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean 3.0807
## P1_1st:units.WAB_20min_mean-WAB_20min_mean 0.4469
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean 1.7482
## VarCompSE
## pop:accession_name.WAB_20min_mean-WAB_20min_mean 0.14368
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_20min_mean-WAB_20min_mean 0.35126
## D1_1st:units.WAB_20min_mean-WAB_20min_mean 0.26797
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean 0.37841
## P1_1st:units.WAB_20min_mean-WAB_20min_mean 0.09984
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean 0.20346
## Zratio
## pop:accession_name.WAB_20min_mean-WAB_20min_mean 7.692
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_20min_mean-WAB_20min_mean 3.073
## D1_1st:units.WAB_20min_mean-WAB_20min_mean 8.910
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean 8.141
## P1_1st:units.WAB_20min_mean-WAB_20min_mean 4.476
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean 8.592
## Constraint
## pop:accession_name.WAB_20min_mean-WAB_20min_mean Positive
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_20min_mean-WAB_20min_mean Positive
## D1_1st:units.WAB_20min_mean-WAB_20min_mean Positive
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean Positive
## P1_1st:units.WAB_20min_mean-WAB_20min_mean Positive
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean Positive
## ========================================================================================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 WAB_20min_mean (Intercept) 2.8124 0.1337 21.0318
## 2 WAB_20min_mean trial_harvestD1_2nd -0.4485 0.1716 -2.6133
## 3 WAB_20min_mean trial_harvestP1_1st -0.5412 0.1218 -4.4433
## 4 WAB_20min_mean trial_harvestP1_2nd -0.5284 0.1423 -3.7124
## 5 WAB_20min_mean check:accession_nameCOL1505 0.7239 0.5495 1.3176
## 6 WAB_20min_mean check:accession_nameCOL1722 2.7641 0.4128 6.6962
## 7 WAB_20min_mean check:accession_nameCOL1910 -1.9140 0.4386 -4.3639
## 8 WAB_20min_mean check:accession_nameCOL2246 -0.1893 0.5118 -0.3699
## ... please access the object to see more
## ========================================================================================================================
## Groups and observations:
## WAB_20min_mean
## pop:accession_name 390
## spl2D(row_number, col_number, at.levels = trial_harvest) 168
## ========================================================================================================================
## Use the '$' sign to access results and parameterscoef.mmer(model.WAB20)## Trait Effect Estimate
## 1 WAB_20min_mean (Intercept) 2.8124402
## 2 WAB_20min_mean trial_harvestD1_2nd -0.4485312
## 3 WAB_20min_mean trial_harvestP1_1st -0.5412128
## 4 WAB_20min_mean trial_harvestP1_2nd -0.5283583
## 5 WAB_20min_mean check:accession_nameCOL1505 0.7239452
## 6 WAB_20min_mean check:accession_nameCOL1722 2.7640615
## 7 WAB_20min_mean check:accession_nameCOL1910 -1.9139502
## 8 WAB_20min_mean check:accession_nameCOL2246 -0.1893205
## 9 WAB_20min_mean check:accession_nameCR138 -1.0103466
## 10 WAB_20min_mean check:accession_namePER183 1.1037378
## 11 WAB_20min_mean check:accession_nameSM3759-36 7.3550936
## 12 WAB_20min_mean check:accession_nameVEN208 1.7915189
## 13 WAB_20min_mean check:accession_nameVEN25 -0.9241181summary(model.WAB20)$varcomp## VarComp
## pop:accession_name.WAB_20min_mean-WAB_20min_mean 1.1052121
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_20min_mean-WAB_20min_mean 1.0793062
## D1_1st:units.WAB_20min_mean-WAB_20min_mean 2.3875305
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean 3.0807322
## P1_1st:units.WAB_20min_mean-WAB_20min_mean 0.4468689
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean 1.7482206
## VarCompSE
## pop:accession_name.WAB_20min_mean-WAB_20min_mean 0.14368059
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_20min_mean-WAB_20min_mean 0.35126179
## D1_1st:units.WAB_20min_mean-WAB_20min_mean 0.26796539
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean 0.37840805
## P1_1st:units.WAB_20min_mean-WAB_20min_mean 0.09984169
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean 0.20345988
## Zratio
## pop:accession_name.WAB_20min_mean-WAB_20min_mean 7.692146
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_20min_mean-WAB_20min_mean 3.072655
## D1_1st:units.WAB_20min_mean-WAB_20min_mean 8.909846
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean 8.141297
## P1_1st:units.WAB_20min_mean-WAB_20min_mean 4.475775
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean 8.592458
## Constraint
## pop:accession_name.WAB_20min_mean-WAB_20min_mean Positive
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_20min_mean-WAB_20min_mean Positive
## D1_1st:units.WAB_20min_mean-WAB_20min_mean Positive
## D1_2nd:units.WAB_20min_mean-WAB_20min_mean Positive
## P1_1st:units.WAB_20min_mean-WAB_20min_mean Positive
## P1_2nd:units.WAB_20min_mean-WAB_20min_mean Positiveheritability.WAB20 <- vpredict(model.WAB20, h2 ~ (V1) / (V1 + (V3+V4+V5+V6)/4))
heritability.WAB20## Estimate SE
## h2 0.3658371 0.03541532BLUPs.WAB20 <- coef.mmer(model.WAB20)$Estimate[1] + randef(model.WAB20)[[1]]$WAB_20min_mean[!names(randef(model.WAB20)[[1]]$WAB_20min_mean) %in% checks]
head(BLUPs.WAB20, 10)## pop:accession_nameGM13159-1 pop:accession_nameGM13159-10
## 1.574262 2.042428
## pop:accession_nameGM13159-11 pop:accession_nameGM13159-12
## 5.641966 1.432623
## pop:accession_nameGM13159-13 pop:accession_nameGM13159-14
## 2.573704 3.778014
## pop:accession_nameGM13159-15 pop:accession_nameGM13159-16
## 2.593442 3.450701
## pop:accession_nameGM13159-17 pop:accession_nameGM13159-18
## 2.989358 2.168107length(BLUPs.WAB20)## [1] 381WAB_30min_mean
model.WAB30 <- mmer(WAB_30min_mean ~ trial_harvest + check:accession_name,
random = ~ pop:accession_name + spl2D(row_number, col_number, at.levels = trial_harvest),
rcov = ~ vs(ds(trial_harvest), units),
data = data.pheno, verbose = TRUE)## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
## iteration LogLik wall cpu(sec) restrained
## 1 -289.928 2:37:9 6 0
## 2 -260.349 2:37:15 12 0
## 3 -250.163 2:37:20 17 0
## 4 -247.031 2:37:24 21 0
## 5 -246.48 2:37:29 26 0
## 6 -246.392 2:37:34 31 0
## 7 -246.379 2:37:39 36 0
## 8 -246.377 2:37:45 42 0
## 9 -246.377 2:37:50 47 0plot(model.WAB30)## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
## fixed-effect model matrix is rank deficient so dropping 381 columns / coefficients
summary(model.WAB30)## ========================================================================================================================
## Multivariate Linear Mixed Model fit by REML
## **************************************************** sommer 4.1 ****************************************************
## ========================================================================================================================
## logLik AIC BIC Method Converge
## Value -246.3767 518.7534 580.8183 NR TRUE
## ========================================================================================================================
## Variance-Covariance components:
## VarComp
## pop:accession_name.WAB_30min_mean-WAB_30min_mean 6.390
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_30min_mean-WAB_30min_mean 6.275
## D1_1st:units.WAB_30min_mean-WAB_30min_mean 21.621
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean 21.404
## P1_1st:units.WAB_30min_mean-WAB_30min_mean 4.919
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean 8.948
## VarCompSE
## pop:accession_name.WAB_30min_mean-WAB_30min_mean 0.9491
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_30min_mean-WAB_30min_mean 2.2615
## D1_1st:units.WAB_30min_mean-WAB_30min_mean 2.3302
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean 2.6352
## P1_1st:units.WAB_30min_mean-WAB_30min_mean 0.8346
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean 1.1345
## Zratio
## pop:accession_name.WAB_30min_mean-WAB_30min_mean 6.732
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_30min_mean-WAB_30min_mean 2.775
## D1_1st:units.WAB_30min_mean-WAB_30min_mean 9.279
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean 8.122
## P1_1st:units.WAB_30min_mean-WAB_30min_mean 5.893
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean 7.887
## Constraint
## pop:accession_name.WAB_30min_mean-WAB_30min_mean Positive
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_30min_mean-WAB_30min_mean Positive
## D1_1st:units.WAB_30min_mean-WAB_30min_mean Positive
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean Positive
## P1_1st:units.WAB_30min_mean-WAB_30min_mean Positive
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean Positive
## ========================================================================================================================
## Fixed effects:
## Trait Effect Estimate Std.Error t.value
## 1 WAB_30min_mean (Intercept) 6.820520 0.3758 18.149604
## 2 WAB_30min_mean trial_harvestD1_2nd -1.389722 0.4765 -2.916619
## 3 WAB_30min_mean trial_harvestP1_1st -2.199545 0.3638 -6.046344
## 4 WAB_30min_mean trial_harvestP1_2nd -2.378249 0.3847 -6.181939
## 5 WAB_30min_mean check:accession_nameCOL1505 4.421027 1.5172 2.914008
## 6 WAB_30min_mean check:accession_nameCOL1722 4.634232 1.0362 4.472403
## 7 WAB_30min_mean check:accession_nameCOL1910 -4.037205 1.1437 -3.529893
## 8 WAB_30min_mean check:accession_nameCOL2246 -0.007587 1.4222 -0.005334
## ... please access the object to see more
## ========================================================================================================================
## Groups and observations:
## WAB_30min_mean
## pop:accession_name 390
## spl2D(row_number, col_number, at.levels = trial_harvest) 168
## ========================================================================================================================
## Use the '$' sign to access results and parameterscoef.mmer(model.WAB30)## Trait Effect Estimate
## 1 WAB_30min_mean (Intercept) 6.820519686
## 2 WAB_30min_mean trial_harvestD1_2nd -1.389721732
## 3 WAB_30min_mean trial_harvestP1_1st -2.199545090
## 4 WAB_30min_mean trial_harvestP1_2nd -2.378249158
## 5 WAB_30min_mean check:accession_nameCOL1505 4.421026774
## 6 WAB_30min_mean check:accession_nameCOL1722 4.634232134
## 7 WAB_30min_mean check:accession_nameCOL1910 -4.037204680
## 8 WAB_30min_mean check:accession_nameCOL2246 -0.007586806
## 9 WAB_30min_mean check:accession_nameCR138 -1.064557076
## 10 WAB_30min_mean check:accession_namePER183 6.189858592
## 11 WAB_30min_mean check:accession_nameSM3759-36 18.767248890
## 12 WAB_30min_mean check:accession_nameVEN208 7.157392630
## 13 WAB_30min_mean check:accession_nameVEN25 -2.922550250summary(model.WAB30)$varcomp## VarComp
## pop:accession_name.WAB_30min_mean-WAB_30min_mean 6.389527
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_30min_mean-WAB_30min_mean 6.274815
## D1_1st:units.WAB_30min_mean-WAB_30min_mean 21.621055
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean 21.403562
## P1_1st:units.WAB_30min_mean-WAB_30min_mean 4.918515
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean 8.947913
## VarCompSE
## pop:accession_name.WAB_30min_mean-WAB_30min_mean 0.9491154
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_30min_mean-WAB_30min_mean 2.2614863
## D1_1st:units.WAB_30min_mean-WAB_30min_mean 2.3301612
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean 2.6351936
## P1_1st:units.WAB_30min_mean-WAB_30min_mean 0.8346329
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean 1.1344607
## Zratio
## pop:accession_name.WAB_30min_mean-WAB_30min_mean 6.732087
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_30min_mean-WAB_30min_mean 2.774642
## D1_1st:units.WAB_30min_mean-WAB_30min_mean 9.278781
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean 8.122197
## P1_1st:units.WAB_30min_mean-WAB_30min_mean 5.893028
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean 7.887372
## Constraint
## pop:accession_name.WAB_30min_mean-WAB_30min_mean Positive
## spl2D(row_number, col_number, at.levels = trial_harvest).WAB_30min_mean-WAB_30min_mean Positive
## D1_1st:units.WAB_30min_mean-WAB_30min_mean Positive
## D1_2nd:units.WAB_30min_mean-WAB_30min_mean Positive
## P1_1st:units.WAB_30min_mean-WAB_30min_mean Positive
## P1_2nd:units.WAB_30min_mean-WAB_30min_mean Positiveheritability.WAB30 <- vpredict(model.WAB30, h2 ~ (V1) / (V1 + (V3+V4+V5+V6)/4))
heritability.WAB30## Estimate SE
## h2 0.3099863 0.0368102BLUPs.WAB30 <- coef.mmer(model.WAB30)$Estimate[1] + randef(model.WAB30)[[1]]$WAB_30min_mean[!names(randef(model.WAB30)[[1]]$WAB_30min_mean) %in% checks]
head(BLUPs.WAB30, 10)## pop:accession_nameGM13159-1 pop:accession_nameGM13159-10
## 4.839227 5.891614
## pop:accession_nameGM13159-11 pop:accession_nameGM13159-12
## 13.574418 4.246389
## pop:accession_nameGM13159-13 pop:accession_nameGM13159-14
## 6.487390 9.658390
## pop:accession_nameGM13159-15 pop:accession_nameGM13159-16
## 5.540753 9.498941
## pop:accession_nameGM13159-17 pop:accession_nameGM13159-18
## 6.414940 6.009262length(BLUPs.WAB30)## [1] 381Heritability table:
heritability <- rbind(heritability.DM, heritability.WAB20, heritability.WAB30)
rownames(heritability) <- c("DM_NIRS", "WAB_20min_mean", "WAB_30min_mean")
knitr::kable(heritability)| Estimate | SE | |
|---|---|---|
| DM_NIRS | 0.5190455 | 0.0320712 |
| WAB_20min_mean | 0.3658371 | 0.0354153 |
| WAB_30min_mean | 0.3099863 | 0.0368102 |
Pairwise correlations:
BLUPs.DM.WAB20.WAB30 <- cbind(BLUPs.DM, BLUPs.WAB20, BLUPs.WAB30)
head(BLUPs.DM.WAB20.WAB30, 10)## BLUPs.DM BLUPs.WAB20 BLUPs.WAB30
## pop:accession_nameGM13159-1 35.87008 1.574262 4.839227
## pop:accession_nameGM13159-10 32.68062 2.042428 5.891614
## pop:accession_nameGM13159-11 37.75610 5.641966 13.574418
## pop:accession_nameGM13159-12 34.68457 1.432623 4.246389
## pop:accession_nameGM13159-13 35.84614 2.573704 6.487390
## pop:accession_nameGM13159-14 42.33356 3.778014 9.658390
## pop:accession_nameGM13159-15 44.24813 2.593442 5.540753
## pop:accession_nameGM13159-16 44.18083 3.450701 9.498941
## pop:accession_nameGM13159-17 42.98113 2.989358 6.414940
## pop:accession_nameGM13159-18 36.27245 2.168107 6.009262rownames(BLUPs.DM.WAB20.WAB30) <- gsub(pattern = "pop:accession_name", replacement = "", rownames(BLUPs.DM.WAB20.WAB30))
colnames(BLUPs.DM.WAB20.WAB30) <- c("DM_NIRS", "WAB_20min_mean", "WAB_30min_mean")
head(BLUPs.DM.WAB20.WAB30)## DM_NIRS WAB_20min_mean WAB_30min_mean
## GM13159-1 35.87008 1.574262 4.839227
## GM13159-10 32.68062 2.042428 5.891614
## GM13159-11 37.75610 5.641966 13.574418
## GM13159-12 34.68457 1.432623 4.246389
## GM13159-13 35.84614 2.573704 6.487390
## GM13159-14 42.33356 3.778014 9.658390correlation <- cor(BLUPs.DM.WAB20.WAB30)
correlation## DM_NIRS WAB_20min_mean WAB_30min_mean
## DM_NIRS 1.0000000 0.5127114 0.3610787
## WAB_20min_mean 0.5127114 1.0000000 0.9148518
## WAB_30min_mean 0.3610787 0.9148518 1.0000000chart.Correlation(BLUPs.DM.WAB20.WAB30, histogram = TRUE, method = "pearson")
Genome-Wide Association Study (GWAS)
Preparing the VCF file
We will use the six full-sib families, which constitute the first population in the VCF. Let’s load the VCF file containing the sample of 9,000 markers and the individuals of both populations:
vcf.file.P1.P2 <- read.vcfR(file = "samplesnp_1.vcf.gz", verbose = TRUE)## Scanning file to determine attributes.
## File attributes:
## meta lines: 114
## header_line: 115
## variant count: 9000
## column count: 618
##
Meta line 114 read in.
## All meta lines processed.
## gt matrix initialized.
## Character matrix gt created.
## Character matrix gt rows: 9000
## Character matrix gt cols: 618
## skip: 0
## nrows: 9000
## row_num: 0
##
Processed variant 1000
Processed variant 2000
Processed variant 3000
Processed variant 4000
Processed variant 5000
Processed variant 6000
Processed variant 7000
Processed variant 8000
Processed variant 9000
Processed variant: 9000
## All variants processedhead(vcf.file.P1.P2)## [1] "***** Object of class 'vcfR' *****"
## [1] "***** Meta section *****"
## [1] "##fileformat=VCFv4.2"
## [1] "##FILTER=<ID=snp_filter,Description=\"QD < 2.0 || FS > 60.0 || MQ < 4 [Truncated]"
## [1] "##FORMAT=<ID=AD,Number=R,Type=Integer,Description=\"Allelic depths fo [Truncated]"
## [1] "##FORMAT=<ID=DP,Number=1,Type=Integer,Description=\"Read depth\">"
## [1] "##FORMAT=<ID=GQ,Number=1,Type=Integer,Description=\"Genotype quality\">"
## [1] "##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">"
## [1] "First 6 rows."
## [1]
## [1] "***** Fixed section *****"
## CHROM POS ID REF ALT QUAL FILTER
## [1,] "chr09" "36007461" NA "A" "G" "35529.40" "PASS"
## [2,] "chr10" "20626079" NA "A" "T" "221719" "PASS"
## [3,] "chr12" "28750390" NA "A" "C" "24385.70" "PASS"
## [4,] "chr17" "1253873" NA "G" "A" "30410" "PASS"
## [5,] "chr15" "25005084" NA "G" "A" "61007.20" "PASS"
## [6,] "chr01" "22541997" NA "C" "T" "77006.90" "PASS"
## [1]
## [1] "***** Genotype section *****"
## FORMAT 202023_100_PER183
## [1,] "GT:AD:DP:GQ:PGT:PID:PL" "0/0:13,0:13:36:.:.:0,36,540"
## [2,] "GT:AD:DP:GQ:PGT:PID:PL" "1/1:0,43:43:99:.:.:1777,132,0"
## [3,] "GT:AD:DP:GQ:PGT:PID:PL" "0/0:24,0:24:63:.:.:0,63,945"
## [4,] "GT:AD:DP:GQ:PGT:PID:PL" "0/1:11,8:19:99:.:.:143,0,201"
## [5,] "GT:AD:DP:GQ:PGT:PID:PL" "0/0:14,0:14:39:.:.:0,39,585"
## [6,] "GT:AD:DP:GQ:PGT:PID:PL" "0/0:15,0:15:36:.:.:0,36,540"
## 202023_101_GM13174-48
## [1,] "1/1:0,7:7:21:1|1:36007449_C_G:315,21,0"
## [2,] "0/1:8,23:31:99:.:.:459,0,102"
## [3,] "0/0:33,0:33:90:.:.:0,90,1350"
## [4,] "0/0:16,0:16:39:.:.:0,39,585"
## [5,] "0/0:5,0:5:15:.:.:0,15,111"
## [6,] "0/0:21,0:21:57:.:.:0,57,855"
## 202023_102_GM13174-49
## [1,] "1/1:0,2:2:6:1|1:36007449_C_G:90,6,0"
## [2,] "0/1:18,62:80:99:.:.:1297,0,205"
## [3,] "0/0:43,0:43:99:.:.:0,102,1530"
## [4,] "0/0:32,0:32:87:.:.:0,87,1305"
## [5,] "0/0:35,0:35:99:.:.:0,99,1485"
## [6,] "0/1:21,15:36:99:.:.:250,0,367"
## 202023_103_GM13174-50
## [1,] "1/1:0,12:12:39:1|1:36007449_C_G:585,39,0"
## [2,] "0/1:25,59:84:99:.:.:1405,0,349"
## [3,] "0/0:64,0:64:99:.:.:0,120,1800"
## [4,] "0/1:20,13:33:99:.:.:298,0,357"
## [5,] "0/0:21,0:21:60:.:.:0,60,900"
## [6,] "0/0:35,0:35:99:.:.:0,99,1485"
## 202023_104_GM13174-51
## [1,] "1/1:0,5:5:15:1|1:36007449_C_G:225,15,0"
## [2,] "0/1:8,18:26:99:.:.:511,0,105"
## [3,] "0/0:11,0:11:30:.:.:0,30,450"
## [4,] "0/1:11,13:24:99:.:.:253,0,191"
## [5,] "1/1:0,12:12:42:1|1:25005084_G_A:440,42,0"
## [6,] "0/1:13,11:24:99:.:.:184,0,217"
## [1] "First 6 columns only."
## [1]
## [1] "Unique GT formats:"
## [1] "GT:AD:DP:GQ:PGT:PID:PL" "GT:AD:DP:GQ:PL"
## [1]Loading the information from population and pedigree of the genotyped individuals:
indiv.vcf <- read.csv(file = "Indiv_VCF_File_Info_Pop_Family.csv", header = TRUE)
head(indiv.vcf)## Indiv_VCF Indiv_VCF_Edit Population Family
## 1 202023_100_PER183 PER183 P1 PER183
## 2 202023_101_GM13174-48 GM13174-48 P1 COL1910-x-SM3759-36
## 3 202023_102_GM13174-49 GM13174-49 P1 COL1910-x-SM3759-36
## 4 202023_103_GM13174-50 GM13174-50 P1 COL1910-x-SM3759-36
## 5 202023_104_GM13174-51 GM13174-51 P1 COL1910-x-SM3759-36
## 6 202023_105_GM13174-52 GM13174-52 P1 COL1910-x-SM3759-36table(indiv.vcf$Population)##
## P1 P2 Parents
## 226 370 13P1 <- which(indiv.vcf$Population == "P1")
Pr <- which(indiv.vcf$Population == "Parents")
indiv.vcf.P1 <- indiv.vcf[c(P1, Pr),]
table(indiv.vcf.P1$Population)##
## P1 Parents
## 226 13Let’s save the individuals from the first population:
corresp.P1 <- colnames(vcf.file.P1.P2@gt) %in% indiv.vcf.P1$Indiv_VCF
columns.to.keep <- c(1,which(corresp.P1 == TRUE))
vcf.file.P1 <- vcf.file.P1.P2 # to get the vcf structure
vcf.file.P1@gt <- vcf.file.P1@gt[,columns.to.keep]
dim(vcf.file.P1@gt) # 227 + 13## [1] 9000 240Checking the amount of individuals for each full-sib family:
split.names <- strsplit(colnames(vcf.file.P1@gt)[-1], split = "_")
split.names <- sapply(split.names, unlist)
split.names <- as.data.frame(t(split.names))
colnames(split.names) <- c("trial","id","ind")
head(split.names)## trial id ind
## 1 202023 100 PER183
## 2 202023 101 GM13174-48
## 3 202023 102 GM13174-49
## 4 202023 103 GM13174-50
## 5 202023 104 GM13174-51
## 6 202023 105 GM13174-52split.names$family <- substr(split.names$ind, 1, 7)
table(split.names$family)##
## COL1722 COL1910 GM13159 GM13169 GM13174 GM13175 GM13240 PER183 SM3759- VEN25
## 5 3 49 24 44 44 62 3 1 4split.names$family[which(split.names$family == "SM3759-")] <- split.names$ind[which(split.names$family == "SM3759-")]To perform association mapping, we need to extract the genotypic data from the original VCF file:
vcf.file.P1.geno <- extract.gt(vcf.file.P1, element = "GT", as.numeric = FALSE, return.alleles = FALSE, IDtoRowNames = TRUE, extract = TRUE, convertNA = FALSE)
vcf.file.P1.geno[1:5,1:5]## 202023_100_PER183 202023_101_GM13174-48 202023_102_GM13174-49
## chr09_36007461 "0/0" "1/1" "1/1"
## chr10_20626079 "1/1" "0/1" "0/1"
## chr12_28750390 "0/0" "0/0" "0/0"
## chr17_1253873 "0/1" "0/0" "0/0"
## chr15_25005084 "0/0" "0/0" "0/0"
## 202023_103_GM13174-50 202023_104_GM13174-51
## chr09_36007461 "1/1" "1/1"
## chr10_20626079 "0/1" "0/1"
## chr12_28750390 "0/0" "0/0"
## chr17_1253873 "0/1" "0/1"
## chr15_25005084 "0/0" "1/1"vcf.file.P1.geno <- apply(vcf.file.P1.geno, 2, function(x) unlist(lapply(strsplit(x, split = ""), function(y) paste(y[c(1,3)], collapse = ""))))
for(i in 1:nrow(vcf.file.P1.geno)) {
vcf.file.P1.geno[i,][which(vcf.file.P1.geno[i,] == "..")] <- NA
}
vcf.file.P1.geno[1:5,1:5]## 202023_100_PER183 202023_101_GM13174-48 202023_102_GM13174-49
## chr09_36007461 "00" "11" "11"
## chr10_20626079 "11" "01" "01"
## chr12_28750390 "00" "00" "00"
## chr17_1253873 "01" "00" "00"
## chr15_25005084 "00" "00" "00"
## 202023_103_GM13174-50 202023_104_GM13174-51
## chr09_36007461 "11" "11"
## chr10_20626079 "01" "01"
## chr12_28750390 "00" "00"
## chr17_1253873 "01" "01"
## chr15_25005084 "00" "11"Let’s check which genotypes were really phenotyped for the traits and only use them to perform GWAS:
BLUEs.DM.WAB <- read.csv(file = "BLUEs_DM_WAB.csv", header = TRUE)
head(BLUEs.DM.WAB)## genotype DM_NIRS WAB_20min_mean WAB_30min_mean
## 1 202023_1_GM13159-1 34.44704 0.9350957 3.384388
## 2 202023_10_COL1722 46.70277 5.6381474 11.787712
## 3 202023_100_PER183 36.91860 3.8691301 12.844435
## 4 202023_101_GM13174-48 32.51729 0.6115424 2.282428
## 5 202023_102_GM13174-49 39.80332 2.4799337 4.255318
## 6 202023_103_GM13174-50 33.85145 0.9029387 2.813686corresp <- colnames(vcf.file.P1.geno) %in% BLUEs.DM.WAB$genotype
vcf.file.P1.geno.DM.WAB <- vcf.file.P1.geno[,which(corresp == TRUE)]
dim(vcf.file.P1.geno.DM.WAB)## [1] 9000 220Since we will use a numeric format (0, 1, 2) for the genotypic data,
it is need to convert the original data from a VCF file. The users can
transform the data by their own and also filter them for some
parameters, such as minor allele frequency (MAF), number of genotypes
and markers to be considered following some proportion of missing data,
and so on. Moreover, a imputation can be done for missing data. For now,
we will transform and filter genotypic data for the numeric format using
the R package statgenGWAS.
# Geno:
vcf.file.P1.geno.DM.WAB <- t(vcf.file.P1.geno.DM.WAB)
dim(vcf.file.P1.geno.DM.WAB)## [1] 220 9000vcf.file.P1.geno.DM.WAB[1:5,1:5]## chr09_36007461 chr10_20626079 chr12_28750390
## 202023_100_PER183 "00" "11" "00"
## 202023_101_GM13174-48 "11" "01" "00"
## 202023_102_GM13174-49 "11" "01" "00"
## 202023_103_GM13174-50 "11" "01" "00"
## 202023_104_GM13174-51 "11" "01" "00"
## chr17_1253873 chr15_25005084
## 202023_100_PER183 "01" "00"
## 202023_101_GM13174-48 "00" "00"
## 202023_102_GM13174-49 "00" "00"
## 202023_103_GM13174-50 "01" "00"
## 202023_104_GM13174-51 "01" "11"vcf.file.P1.geno.DM.WAB <- vcf.file.P1.geno.DM.WAB[,order(colnames(vcf.file.P1.geno.DM.WAB))]
vcf.file.P1.geno.DM.WAB[1:5,1:5]## chr01_10004826 chr01_10151663 chr01_10367331
## 202023_100_PER183 "01" "00" "00"
## 202023_101_GM13174-48 "01" "00" "00"
## 202023_102_GM13174-49 "01" "00" "00"
## 202023_103_GM13174-50 "01" "00" "00"
## 202023_104_GM13174-51 "01" "00" "00"
## chr01_10386693 chr01_10394249
## 202023_100_PER183 "00" "00"
## 202023_101_GM13174-48 "01" "01"
## 202023_102_GM13174-49 "01" "01"
## 202023_103_GM13174-50 "01" "01"
## 202023_104_GM13174-51 "00" "00"# Map:
spliting.names <- strsplit(colnames(vcf.file.P1.geno.DM.WAB), split = "")
chr <- unlist(lapply(spliting.names, function(x) paste(x[(which(x == "_")-2):(which(x == "_")-1)],
collapse = "")))
pos <- unlist(lapply(spliting.names, function(x) paste(x[(which(x == "_")+1):(length(x))],
collapse = "")))
table(chr)## chr
## 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18
## 598 605 456 522 452 475 547 546 485 435 473 585 535 460 488 470 449 419map <- data.frame(chr = chr, pos = pos)
head(map)## chr pos
## 1 01 10004826
## 2 01 10151663
## 3 01 10367331
## 4 01 10386693
## 5 01 10394249
## 6 01 10459214dim(map)## [1] 9000 2rownames(map) <- colnames(vcf.file.P1.geno.DM.WAB)
head(map)## chr pos
## chr01_10004826 01 10004826
## chr01_10151663 01 10151663
## chr01_10367331 01 10367331
## chr01_10386693 01 10386693
## chr01_10394249 01 10394249
## chr01_10459214 01 10459214str(map)## 'data.frame': 9000 obs. of 2 variables:
## $ chr: chr "01" "01" "01" "01" ...
## $ pos: chr "10004826" "10151663" "10367331" "10386693" ...map$pos <- as.numeric(map$pos)cassava.data <- createGData(geno = vcf.file.P1.geno.DM.WAB, map = map)cassava.data.coded <- codeMarkers(gData = cassava.data, refAll = rep("0", times = ncol(vcf.file.P1.geno.DM.WAB)),
nMissGeno = 0.40, nMiss = 0.50, impute = TRUE, imputeType = "fixed", fixedValue = 1,
MAF = 0.05, removeDuplicates = TRUE, verbose = TRUE)## Input contains 9000 SNPs for 220 genotypes.
## 0 genotypes removed because proportion of missing values larger than or equal to 0.4.
## 0 SNPs removed because proportion of missing values larger than or equal to 0.5.
## 1141 SNPs removed because MAF smaller than 0.05.
## 98 duplicate SNPs removed.
## 23069 missing values imputed.
## 0 SNPs removed because MAF smaller than 0.05 after imputation.
## 9 duplicate SNPs removed after imputation.
## Output contains 7752 SNPs for 220 genotypes.With the genotypic data in numeric format, we can prepare the files
for running GWAS in the R package rMVP.
# Geno:
data.for.rMVP <- cassava.data.coded$markers
dim(data.for.rMVP)## [1] 220 7752data.for.rMVP[1:5,1:5]## chr01_10004826 chr01_10151663 chr01_10367331
## 202023_1_GM13159-1 0 2 2
## 202023_10_COL1722 0 2 0
## 202023_100_PER183 1 2 2
## 202023_101_GM13174-48 1 2 2
## 202023_102_GM13174-49 1 2 2
## chr01_10386693 chr01_10394249
## 202023_1_GM13159-1 1 2
## 202023_10_COL1722 1 2
## 202023_100_PER183 2 2
## 202023_101_GM13174-48 1 1
## 202023_102_GM13174-49 1 1data.for.rMVP <- t(data.for.rMVP)
dim(data.for.rMVP)## [1] 7752 220data.for.rMVP[1:5,1:5]## 202023_1_GM13159-1 202023_10_COL1722 202023_100_PER183
## chr01_10004826 0 0 1
## chr01_10151663 2 2 2
## chr01_10367331 2 0 2
## chr01_10386693 1 1 2
## chr01_10394249 2 2 2
## 202023_101_GM13174-48 202023_102_GM13174-49
## chr01_10004826 1 1
## chr01_10151663 2 2
## chr01_10367331 2 2
## chr01_10386693 1 1
## chr01_10394249 1 1# Map:
map.for.rMVP <- map
dim(map.for.rMVP)## [1] 9000 2head(map.for.rMVP)## chr pos
## chr01_10004826 01 10004826
## chr01_10151663 01 10151663
## chr01_10367331 01 10367331
## chr01_10386693 01 10386693
## chr01_10394249 01 10394249
## chr01_10459214 01 10459214map.for.rMVP$Pos <- map.for.rMVP$pos
map.for.rMVP <- map.for.rMVP[,-2]
map.for.rMVP$SNP <- rownames(map.for.rMVP)
map.for.rMVP <- map.for.rMVP[rownames(data.for.rMVP),]
rownames(map.for.rMVP) <- 1:nrow(map.for.rMVP)
colnames(map.for.rMVP)[1] <- "Chr"
map.for.rMVP <- map.for.rMVP[,c("SNP","Chr","Pos")]
head(map.for.rMVP)## SNP Chr Pos
## 1 chr01_10004826 01 10004826
## 2 chr01_10151663 01 10151663
## 3 chr01_10367331 01 10367331
## 4 chr01_10386693 01 10386693
## 5 chr01_10394249 01 10394249
## 6 chr01_10459214 01 10459214str(map.for.rMVP)## 'data.frame': 7752 obs. of 3 variables:
## $ SNP: chr "chr01_10004826" "chr01_10151663" "chr01_10367331" "chr01_10386693" ...
## $ Chr: chr "01" "01" "01" "01" ...
## $ Pos: num 10004826 10151663 10367331 10386693 10394249 ...dim(map.for.rMVP)## [1] 7752 3write.table(map.for.rMVP, file = "Map_For_rMVP.txt", row.names = FALSE, sep = "\t")For a suitable reading of data from R package rMVP, it
is needed to save the file as a big matrix:
data.for.rMVP <- as.matrix(data.for.rMVP)
data.for.rMVP[1:5,1:5]## 202023_1_GM13159-1 202023_10_COL1722 202023_100_PER183
## chr01_10004826 0 0 1
## chr01_10151663 2 2 2
## chr01_10367331 2 0 2
## chr01_10386693 1 1 2
## chr01_10394249 2 2 2
## 202023_101_GM13174-48 202023_102_GM13174-49
## chr01_10004826 1 1
## chr01_10151663 2 2
## chr01_10367331 2 2
## chr01_10386693 1 1
## chr01_10394249 1 1data.for.rMVP <- as.big.matrix(data.for.rMVP)
data.for.rMVP[1:5,1:5]## 202023_1_GM13159-1 202023_10_COL1722 202023_100_PER183
## chr01_10004826 0 0 1
## chr01_10151663 2 2 2
## chr01_10367331 2 0 2
## chr01_10386693 1 1 2
## chr01_10394249 2 2 2
## 202023_101_GM13174-48 202023_102_GM13174-49
## chr01_10004826 1 1
## chr01_10151663 2 2
## chr01_10367331 2 2
## chr01_10386693 1 1
## chr01_10394249 1 1write.big.matrix(data.for.rMVP, file = "Data_For_rMVP.txt", row.names = FALSE, sep = "\t")Preparing files in the R package rMVP for run GWAS:
MVP.Data(fileNum = "Data_For_rMVP.txt", filePhe = "BLUEs_DM_WAB.csv", fileMap = "Map_For_rMVP.txt",
sep.num = "\t", sep.phe = ",", sep.map = "\t", SNP.impute = NULL, fileKin = FALSE, filePC = FALSE,
out = "mvp", priority = "speed", verbose = TRUE)## Preparing data for MVP...
## Reading file...
## inds: 220 markers:7752
## Preparation for GENOTYPE data is done within 1s
## Preparation for PHENOTYPE data is Done within 0s
## MVP data prepration accomplished successfully!MVP.Data.Kin(fileKin = TRUE, sep = "\t", mvp_prefix = "mvp", out = "mvp",
priority = "speed", verbose = TRUE)## Calculate KINSHIP using Vanraden method...
## Computing GRM under mode: Speed
## Scale the genotype matrix
## Computing Z'Z
## Deriving relationship matrix successfully
## Preparation for Kinship matrix is done!## An object of class "big.matrix"
## Slot "address":
## <pointer: 0x00000000298633f0>MVP.Data.PC(filePC = TRUE, sep = "\t", pcs.keep = 5, mvp_prefix = "mvp", out = "mvp",
priority = "speed", verbose = TRUE)## Computing GRM under mode: Speed
## Scale the genotype matrix
## Computing Z'Z
## Deriving relationship matrix successfully
## Eigen Decomposition on GRM
## Deriving PCs successfully
## Preparation for PC matrix is done!Let’s load the files created by R package rMVP in the
current directory:
genotype <- attach.big.matrix("mvp.geno.desc")
phenotype <- read.table("mvp.phe", head = TRUE)
map <- read.table("mvp.geno.map", head = TRUE)
kinship <- attach.big.matrix("mvp.kin.desc")Genomic-based Relationship Matrix (K Matrix)
kinship.plot <- MVP.K.VanRaden(M = genotype, priority = "speed", cpu = 1, verbose = TRUE)## Computing GRM under mode: Speed
## Scale the genotype matrix
## Computing Z'Z
## Deriving relationship matrix successfullydim(kinship.plot)## [1] 220 220kinship.plot[1:5,1:5]## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.79759277 -0.13057867 -0.09066269 -0.01227824 0.09039744
## [2,] -0.13057867 1.18495572 0.12611138 -0.07782971 -0.09098944
## [3,] -0.09066269 0.12611138 1.28862520 -0.01631098 -0.03729239
## [4,] -0.01227824 -0.07782971 -0.01631098 0.90495299 0.32974942
## [5,] 0.09039744 -0.09098944 -0.03729239 0.32974942 0.88980737rownames(kinship.plot) <- colnames(data.for.rMVP)
colnames(kinship.plot) <- colnames(data.for.rMVP)
heatmap(kinship.plot, Rowv = TRUE, Colv = TRUE)
Population Structure (Q Matrix)
Although the R package rMVP will automatically create a
PCA plot in the current directory, from the run of its GWAS function, we
will build it by ourselves using the principal components calculated by
rMVP. Thus, it will be possible to input and view the
clustering patterns considering particular filtering, such as different
fullsib families, which constitute the first population (P1):
pca <- MVP.PCA(M = genotype, priority = "speed", pcs.keep = 5, cpu = 1, verbose = TRUE)## Computing GRM under mode: Speed
## Scale the genotype matrix
## Computing Z'Z
## Deriving relationship matrix successfully
## Eigen Decomposition on GRM
## Deriving PCs successfullyhead(pca)## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.023973749 0.09454377 0.02415545 0.08622249 -0.015670244
## [2,] -0.009518504 -0.06762087 0.18683689 -0.09775046 0.012224385
## [3,] -0.007393545 -0.03154574 0.04407003 -0.01051980 0.018478677
## [4,] -0.084794346 0.05234778 -0.01680075 -0.07072566 0.081086945
## [5,] -0.079858265 0.05609353 -0.02496888 -0.06410526 -0.006268762
## [6,] -0.080535903 0.05698547 -0.01866969 -0.05947868 -0.039488964dim(pca)## [1] 220 5rownames(pca) <- colnames(data.for.rMVP)
colnames(pca) <- c("PC1", "PC2", "PC3", "PC4", "PC5")
head(pca)## PC1 PC2 PC3 PC4
## 202023_1_GM13159-1 0.023973749 0.09454377 0.02415545 0.08622249
## 202023_10_COL1722 -0.009518504 -0.06762087 0.18683689 -0.09775046
## 202023_100_PER183 -0.007393545 -0.03154574 0.04407003 -0.01051980
## 202023_101_GM13174-48 -0.084794346 0.05234778 -0.01680075 -0.07072566
## 202023_102_GM13174-49 -0.079858265 0.05609353 -0.02496888 -0.06410526
## 202023_103_GM13174-50 -0.080535903 0.05698547 -0.01866969 -0.05947868
## PC5
## 202023_1_GM13159-1 -0.015670244
## 202023_10_COL1722 0.012224385
## 202023_100_PER183 0.018478677
## 202023_101_GM13174-48 0.081086945
## 202023_102_GM13174-49 -0.006268762
## 202023_103_GM13174-50 -0.039488964pca <- as.data.frame(pca)
indiv.vcf.P1.pca <- indiv.vcf.P1[which(indiv.vcf.P1$Indiv_VCF %in% rownames(pca) == TRUE),]
indiv.vcf.P1.pca <- indiv.vcf.P1.pca[order(indiv.vcf.P1.pca$Indiv_VCF),]
pca <- pca[order(rownames(pca)),]
head(cbind(indiv.vcf.P1.pca$Indiv_VCF, rownames(pca)), 20)## [,1] [,2]
## [1,] "202023_1_GM13159-1" "202023_1_GM13159-1"
## [2,] "202023_10_COL1722" "202023_10_COL1722"
## [3,] "202023_100_PER183" "202023_100_PER183"
## [4,] "202023_101_GM13174-48" "202023_101_GM13174-48"
## [5,] "202023_102_GM13174-49" "202023_102_GM13174-49"
## [6,] "202023_103_GM13174-50" "202023_103_GM13174-50"
## [7,] "202023_104_GM13174-51" "202023_104_GM13174-51"
## [8,] "202023_105_GM13174-52" "202023_105_GM13174-52"
## [9,] "202023_106_GM13174-53" "202023_106_GM13174-53"
## [10,] "202023_107_GM13174-54" "202023_107_GM13174-54"
## [11,] "202023_108_GM13174-55" "202023_108_GM13174-55"
## [12,] "202023_11_GM13159-10" "202023_11_GM13159-10"
## [13,] "202023_110_COL1910" "202023_110_COL1910"
## [14,] "202023_111_GM13175-1" "202023_111_GM13175-1"
## [15,] "202023_112_GM13175-2" "202023_112_GM13175-2"
## [16,] "202023_113_GM13175-3" "202023_113_GM13175-3"
## [17,] "202023_114_GM13175-4" "202023_114_GM13175-4"
## [18,] "202023_115_GM13175-5" "202023_115_GM13175-5"
## [19,] "202023_116_GM13175-6" "202023_116_GM13175-6"
## [20,] "202023_117_GM13175-7" "202023_117_GM13175-7"pca$Family <- indiv.vcf.P1.pca$Family
head(pca)## PC1 PC2 PC3 PC4
## 202023_1_GM13159-1 0.023973749 0.09454377 0.02415545 0.08622249
## 202023_10_COL1722 -0.009518504 -0.06762087 0.18683689 -0.09775046
## 202023_100_PER183 -0.007393545 -0.03154574 0.04407003 -0.01051980
## 202023_101_GM13174-48 -0.084794346 0.05234778 -0.01680075 -0.07072566
## 202023_102_GM13174-49 -0.079858265 0.05609353 -0.02496888 -0.06410526
## 202023_103_GM13174-50 -0.080535903 0.05698547 -0.01866969 -0.05947868
## PC5 Family
## 202023_1_GM13159-1 -0.015670244 SM3759-36-x-VEN25
## 202023_10_COL1722 0.012224385 COL1722
## 202023_100_PER183 0.018478677 PER183
## 202023_101_GM13174-48 0.081086945 COL1910-x-SM3759-36
## 202023_102_GM13174-49 -0.006268762 COL1910-x-SM3759-36
## 202023_103_GM13174-50 -0.039488964 COL1910-x-SM3759-36ggplot(pca, aes(PC1, PC2, colour = Family)) + geom_point(size = 2, shape = 1, alpha = 0.7) +
scale_color_manual(values = brewer.pal(n = 11, "Paired")) +
xlab("PC1") + ylab("PC2") +
theme_bw()
Let’s check the proportion explained by the first three principal
components. We are going to run PCA again using the R function
prcomp, because the function from rMVP does
not provide the variance of these components.
prin.comp <- prcomp(kinship.plot, scale = TRUE)
eig <- get_eigenvalue(prin.comp)
round(sum(eig$variance.percent[1]),1)## [1] 53round(sum(eig$variance.percent[2]),1)## [1] 31.2round(sum(eig$variance.percent[1:3]),1)## [1] 91.9fviz_eig(prin.comp)
GWAS Analyses
Running GWAS analyses for the traits DM_NIRS,
WAB_20min_mean, and WAB_30min_mean:
GWAS.rMVP <- vector("list", ncol(phenotype)-1)
for(i in 2:ncol(phenotype)) {
GWAS.rMVP[[i-1]] <- MVP(phe = phenotype[,c(1,i)],
geno = genotype,
map = map,
K = kinship,
nPC.GLM = 5,
nPC.MLM = 3,
nPC.FarmCPU = 3,
#CV.GLM = NULL,
#CV.MLM = NULL,
#CV.FarmCPU = NULL,
priority = "speed",
ncpus = 1,
vc.method = "BRENT",
maxLoop = 12,
method.bin = "static",
permutation.threshold = TRUE,
permutation.rep = 1000,
method = c("GLM","MLM","FarmCPU"),
file.output = c("pmap","pmap.signal","plot","log"),
verbose = FALSE)
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## scanning...## PCA plot2d## SNP_Density Plotting## Circular_Manhattan Plotting DM_NIRS.GLM
## Circular_Manhattan Plotting DM_NIRS.MLM
## Circular_Manhattan Plotting DM_NIRS.FarmCPU
## Rectangular_Manhattan Plotting DM_NIRS.GLM## Rectangular_Manhattan Plotting DM_NIRS.MLM## Rectangular_Manhattan Plotting DM_NIRS.FarmCPU## Q_Q Plotting DM_NIRS.GLM## Q_Q Plotting DM_NIRS.MLM## Q_Q Plotting DM_NIRS.FarmCPU## Multracks_Rectangular Plotting DM_NIRS.GLM## Multracks_Rectangular Plotting DM_NIRS.MLM## Multracks_Rectangular Plotting DM_NIRS.FarmCPU## Multraits_Rectangular Plotting...(finished 14%)
Multraits_Rectangular Plotting...(finished 27%)
Multraits_Rectangular Plotting...(finished 40%)
Multraits_Rectangular Plotting...(finished 53%)
Multraits_Rectangular Plotting...(finished 65%)
Multraits_Rectangular Plotting...(finished 78%)
Multraits_Rectangular Plotting...(finished 91%)
Multraits_Rectangular Plotting...(finished 100%)
## Multracks_QQ Plotting DM_NIRS.GLM## Multracks_QQ Plotting DM_NIRS.MLM## Multracks_QQ Plotting DM_NIRS.FarmCPU## Multraits_QQ Plotting DM_NIRS.GLM
## Multraits_QQ Plotting DM_NIRS.MLM
## Multraits_QQ Plotting DM_NIRS.FarmCPU
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## scanning...## PCA plot2d## SNP_Density Plotting## Circular_Manhattan Plotting WAB_20min_mean.GLM
## Circular_Manhattan Plotting WAB_20min_mean.MLM
## Circular_Manhattan Plotting WAB_20min_mean.FarmCPU
## Rectangular_Manhattan Plotting WAB_20min_mean.GLM## Rectangular_Manhattan Plotting WAB_20min_mean.MLM## Rectangular_Manhattan Plotting WAB_20min_mean.FarmCPU## Q_Q Plotting WAB_20min_mean.GLM## Q_Q Plotting WAB_20min_mean.MLM## Q_Q Plotting WAB_20min_mean.FarmCPU## Multracks_Rectangular Plotting WAB_20min_mean.GLM## Multracks_Rectangular Plotting WAB_20min_mean.MLM## Multracks_Rectangular Plotting WAB_20min_mean.FarmCPU## Multraits_Rectangular Plotting...(finished 14%)
Multraits_Rectangular Plotting...(finished 27%)
Multraits_Rectangular Plotting...(finished 39%)
Multraits_Rectangular Plotting...(finished 52%)
Multraits_Rectangular Plotting...(finished 65%)
Multraits_Rectangular Plotting...(finished 78%)
Multraits_Rectangular Plotting...(finished 91%)
Multraits_Rectangular Plotting...(finished 100%)
## Multracks_QQ Plotting WAB_20min_mean.GLM## Multracks_QQ Plotting WAB_20min_mean.MLM## Multracks_QQ Plotting WAB_20min_mean.FarmCPU## Multraits_QQ Plotting WAB_20min_mean.GLM
## Multraits_QQ Plotting WAB_20min_mean.MLM
## Multraits_QQ Plotting WAB_20min_mean.FarmCPU
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## scanning...## PCA plot2d## SNP_Density Plotting## Circular_Manhattan Plotting WAB_30min_mean.GLM
## Circular_Manhattan Plotting WAB_30min_mean.MLM
## Circular_Manhattan Plotting WAB_30min_mean.FarmCPU
## Rectangular_Manhattan Plotting WAB_30min_mean.GLM## Rectangular_Manhattan Plotting WAB_30min_mean.MLM## Rectangular_Manhattan Plotting WAB_30min_mean.FarmCPU## Q_Q Plotting WAB_30min_mean.GLM## Q_Q Plotting WAB_30min_mean.MLM## Q_Q Plotting WAB_30min_mean.FarmCPU## Multracks_Rectangular Plotting WAB_30min_mean.GLM## Multracks_Rectangular Plotting WAB_30min_mean.MLM## Multracks_Rectangular Plotting WAB_30min_mean.FarmCPU## Multraits_Rectangular Plotting...(finished 13%)
Multraits_Rectangular Plotting...(finished 26%)
Multraits_Rectangular Plotting...(finished 39%)
Multraits_Rectangular Plotting...(finished 52%)
Multraits_Rectangular Plotting...(finished 65%)
Multraits_Rectangular Plotting...(finished 78%)
Multraits_Rectangular Plotting...(finished 91%)
Multraits_Rectangular Plotting...(finished 100%)
## Multracks_QQ Plotting WAB_30min_mean.GLM## Multracks_QQ Plotting WAB_30min_mean.MLM## Multracks_QQ Plotting WAB_30min_mean.FarmCPU## Multraits_QQ Plotting WAB_30min_mean.GLM
## Multraits_QQ Plotting WAB_30min_mean.MLM
## Multraits_QQ Plotting WAB_30min_mean.FarmCPUsave(GWAS.rMVP, file = "GWAS_rMVP.RData")Density Plot:
Let’s investigate the GWAS results for the traits
DM_NIRS, WAB_20min_mean, and
WAB_30min_mean, which were also considered for QTL
mapping.
DM_NIRS
QQplot of GWAS models for DM_NIRS:
Manhattan Plot of GWAS models for DM_NIRS:
Statistically significant SNPs additive effects for DM_NIRS:
DM.NIRS.GLM.signals <- read.csv(file = "DM_NIRS.GLM_signals.csv", header = TRUE)
DM.NIRS.GLM.signals## SNP Chr Pos Effect SE DM_NIRS.GLM
## 1 chr01_31498756 1 31498756 -2.302833 0.3282478 2.999770e-11
## 2 chr01_31544211 1 31544211 2.218736 0.4140654 2.172779e-07
## 3 chr01_32001190 1 32001190 3.582134 0.5491636 4.937882e-10
## 4 chr01_32687089 1 32687089 2.628886 0.5077581 5.195382e-07
## 5 chr01_32694239 1 32694239 3.763603 0.6199308 5.755794e-09
## 6 chr01_33067627 1 33067627 -2.732186 0.4579702 1.002534e-08
## 7 chr01_33222617 1 33222617 2.597263 0.5047489 6.041679e-07
## 8 chr01_33244439 1 33244439 -6.914814 0.7013632 3.955384e-19
## 9 chr01_33250754 1 33250754 2.145098 0.4320971 1.410393e-06
## 10 chr01_33359326 1 33359326 3.946389 0.5631571 3.140926e-11
## 11 chr01_34671057 1 34671057 -2.364503 0.4626556 7.126068e-07
## 12 chr01_34963322 1 34963322 -4.121935 0.5974487 5.875996e-11
## 13 chr01_35951952 1 35951952 -3.170426 0.5464249 2.348455e-08
## 14 chr01_36342222 1 36342222 -2.455689 0.5095150 2.731641e-06
## 15 chr01_36478419 1 36478419 -2.892930 0.5553346 4.462114e-07
## 16 chr01_36754746 1 36754746 -2.809876 0.5562317 9.404264e-07
## 17 chr01_37058184 1 37058184 -5.225893 0.7809862 1.920962e-10
## 18 chr01_37225079 1 37225079 -3.919845 0.7402210 2.948970e-07
## 19 chr01_37330141 1 37330141 -2.767392 0.5609267 1.625234e-06
## 20 chr01_37460442 1 37460442 -3.726157 0.6906101 1.813031e-07
## 21 chr01_37601557 1 37601557 -2.808490 0.5505880 7.463756e-07DM.NIRS.MLM.signals <- read.csv(file = "DM_NIRS.MLM_signals.csv", header = TRUE)
DM.NIRS.MLM.signals## SNP Chr Pos Effect SE DM_NIRS.MLM
## 1 chr01_31498756 1 31498756 -2.208510 0.3771795 1.764330e-08
## 2 chr01_31544211 1 31544211 2.087751 0.4588235 8.957437e-06
## 3 chr01_32001190 1 32001190 3.574365 0.6162618 2.347935e-08
## 4 chr01_32687089 1 32687089 2.689982 0.5628273 3.256917e-06
## 5 chr01_32694239 1 32694239 3.736247 0.6910184 1.700010e-07
## 6 chr01_33067627 1 33067627 -2.939845 0.5160218 3.979067e-08
## 7 chr01_33222617 1 33222617 2.526538 0.5499744 7.407272e-06
## 8 chr01_33244439 1 33244439 -6.775264 0.8824841 5.627564e-13
## 9 chr01_33359326 1 33359326 3.853750 0.6491168 1.153116e-08
## 10 chr01_34671057 1 34671057 -2.633247 0.5268758 1.200412e-06
## 11 chr01_34963322 1 34963322 -4.463881 0.7115745 1.917253e-09
## 12 chr01_35951952 1 35951952 -3.380850 0.6413435 3.285720e-07
## 13 chr01_36478419 1 36478419 -3.093867 0.6441909 2.932458e-06
## 14 chr01_36754746 1 36754746 -3.023509 0.6439496 4.742356e-06
## 15 chr01_37058184 1 37058184 -5.380873 0.9206174 1.862806e-08
## 16 chr01_37225079 1 37225079 -4.162402 0.8547003 2.161373e-06
## 17 chr01_37330141 1 37330141 -3.041516 0.6597242 6.895983e-06
## 18 chr01_37460442 1 37460442 -3.790667 0.7788896 2.193667e-06
## 19 chr01_37601557 1 37601557 -2.864610 0.6270143 8.269063e-06DM.NIRS.FarmCPU.signals <- read.csv(file = "DM_NIRS.FarmCPU_signals.csv", header = TRUE)
DM.NIRS.FarmCPU.signals## SNP Chr Pos Effect SE DM_NIRS.FarmCPU
## 1 chr01_33244439 1 33244439 -4.794971 0.6203425 2.670378e-14
## 2 chr17_34503075 17 34503075 2.067476 0.5056101 5.395649e-06WAB_20min_mean
QQplot of GWAS models for WAB_20min_mean:
Manhattan Plot of GWAS models for WAB_20min_mean:
Statistically significant SNPs additive effects for WAB_20min_mean:
WAB.20min.mean.GLM.signals <- read.csv(file = "WAB_20min_mean.GLM_signals.csv", header = TRUE)
WAB.20min.mean.GLM.signals## SNP Chr Pos Effect SE WAB_20min_mean.GLM
## 1 chr01_31498756 1 31498756 -0.5635170 0.1141130 1.591022e-06
## 2 chr01_31544211 1 31544211 0.6670322 0.1385579 2.801035e-06
## 3 chr01_32001190 1 32001190 1.0545634 0.1853813 4.197399e-08
## 4 chr01_32694239 1 32694239 1.0433538 0.2102696 1.426165e-06
## 5 chr01_33244439 1 33244439 -1.7697750 0.2523417 3.037074e-11
## 6 chr01_37058184 1 37058184 -1.2859028 0.2701833 3.582414e-06
## 7 chr01_37330141 1 37330141 -0.9254164 0.1853055 1.229816e-06WAB.20min.mean.MLM.signals <- read.csv(file = "WAB_20min_mean.MLM_signals.csv", header = TRUE)
WAB.20min.mean.MLM.signals## SNP Chr Pos Effect SE WAB_20min_mean.MLM
## 1 chr01_31498756 1 31498756 -0.5817123 0.1220193 3.437657e-06
## 2 chr01_32001190 1 32001190 1.0668656 0.2000251 2.431516e-07
## 3 chr01_32694239 1 32694239 1.0678013 0.2244055 3.579598e-06
## 4 chr01_33244439 1 33244439 -1.7769769 0.2876506 3.215380e-09
## 5 chr01_37330141 1 37330141 -0.9730553 0.2095997 5.988393e-06WAB.20min.mean.FarmCPU.signals <- read.csv(file = "WAB_20min_mean.FarmCPU_signals.csv", header = TRUE)
WAB.20min.mean.FarmCPU.signals## SNP Chr Pos Effect SE WAB_20min_mean.FarmCPU
## 1 chr01_20330444 1 20330444 -0.7509603 0.1662814 3.072326e-07
## 2 chr01_33244439 1 33244439 -1.6220867 0.1986882 4.265462e-16
## 3 chr10_24095256 10 24095256 1.4701395 0.2662429 6.968733e-09
## 4 chr12_4816670 12 4816670 -0.8644900 0.1596741 1.424908e-08WAB_30min_mean
QQplot of GWAS models for WAB_30min_mean:
Manhattan Plot of GWAS models for WAB_30min_mean:
Statistically significant SNPs additive effects for WAB_30min_mean:
WAB.30min.mean.GLM.signals <- read.csv(file = "WAB_30min_mean.GLM_signals.csv", header = TRUE)
WAB.30min.mean.GLM.signals## SNP Chr Pos Effect SE WAB_30min_mean.GLM
## 1 chr01_33244439 1 33244439 -3.669480 0.6742391 1.439545e-07
## 2 chr12_4816670 12 4816670 -2.470494 0.4783013 5.508152e-07WAB.30min.mean.MLM.signals <- read.csv(file = "WAB_30min_mean.MLM_signals.csv", header = TRUE)
WAB.30min.mean.MLM.signals## SNP Chr Pos Effect SE WAB_30min_mean.MLM
## 1 chr01_33244439 1 33244439 -3.649481 0.7495516 2.172507e-06WAB.30min.mean.FarmCPU.signals <- read.csv(file = "WAB_30min_mean.FarmCPU_signals.csv", header = TRUE)
WAB.30min.mean.FarmCPU.signals## SNP Chr Pos Effect SE WAB_30min_mean.FarmCPU
## 1 chr01_33244439 1 33244439 -3.186922 0.536744 4.552646e-10
## 2 chr03_28198260 3 28198260 -6.698192 1.787558 1.177327e-07